Method for detecting epileptic and psychogenic seizures

Abstract

The invention relates to a method for detecting epileptic or psychogenic seizures, comprising the steps of: a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, the series of temporally sequential parameter values preferably differing in that the series that served as the basis for determining the following parameter value includes, in place of the oldest RR interval of the series that served as the basis for determining the preceding parameter value (preceding series), the RR interval temporally subsequent to the most recent RR interval of the preceding series, b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values), c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure.

The invention claimed is: 
     
       1. Method for detecting epileptic or psychogenic seizures, comprising the steps of:
 a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, wherein the parameter type is a mean value, a standard deviation, a cardiac sympathetic index (CSI), a functionally modified cardiac sympathetic index (CSImod), a cardiovascular index, a Baevsky stress index, an adequacy index of regulatory processes, a vegetative equilibrium index, a vegetative rhythm index, a square root of a mean of a sum of all squared differences between successive RR intervals (RMSSD), a skewness of a distribution of the RR intervals (RRSkew), a kurtosis of the distribution of the RR intervals (RRKurt), a percentage of intervals differing by at least 50 milliseconds from the preceding interval (pNN50), a number of pairs of successive RR intervals that throughout the recording differ from one another by more than 50 milliseconds (NN50), a frequency range below 0.003 hertz (ULF), a 0.003 to 0.04 hertz frequency range (VLF), a 0.04 to 0.15 hertz frequency range (LF), a 0.14 to 0.4 hertz frequency range (HF), a ratio of LF to HF, an absolute spectral power density (ttlpwr), a most strongly represented spectral power density from a low-frequency band and from a high-frequency band, a most strongly occurring frequency in the low-and high-frequency band, an acceleration and deceleration capacity of a cardiac rhythm based on RR intervals (PRSA-AC and PRSA-DC), a sample entropy (SampEn), an approximate entropy (ApEn), a coefficient of sample entropy (CoSEN), a long-term and short-term detrended fluctuation analysis (DFA), an integral of a density distribution or a triangular index (TRI), an approximate length and width of an ellipse of a Poincaré plot and ratio thereof, a heart rate turbulence (HRT) or a recurrence rate (REC), 
 b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values), 
 c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure. 
 
     
     
       2. Method according to  claim 1 , comprising the following steps
 d. recording a large number of temporally sequential parameter values for a second parameter type (second-parameter values) in accordance with method step a, 
 e. comparing the time course of the values for the second parameter with the time course of parameter values for the second parameter type that had been determined in accordance with method step a, and the determination thereof is based on RR intervals that indicate a seizure (second-parameter reference values), 
 a seizure according to method step d. being identified only when the time course of the values for the second parameter additionally exhibits a characteristic of the time course of the second-parameter reference values (course characteristic for second-parameter values) indicating a seizure. 
 
     
     
       3. Method according to  claim 2 , additionally comprising the following steps
 f. performing steps d. and e. in an analogous manner for at least a third parameter type, a seizure according to method step c. being identified only when the time course of the values for the third parameter additionally exhibits a characteristic of the time course of the third-parameter reference values (course characteristic for third-parameter values) indicating a seizure or 
 a seizure according to method step c. being identified when the time course of the values for the third parameter does not exhibit a course characteristic for third-parameter values. 
 
     
     
       4. Method according to  claim 3 , wherein the parameter type of the (first) parameter values is the mean value, the parameter type of the second parameter values is the standard deviation and the parameter type of the third parameter values is the CSI. 
     
     
       5. Method according to  claim 1 , wherein the parameter type is the mean value, the standard deviation, the cardiac sympathetic index (CSI), the functionally modified cardiac sympathetic index (CSImod), the cardiovascular index, the Baevsky stress index, the adequacy index of regulatory processes, the vegetative equilibrium index or the vegetative rhythm index. 
     
     
       6. Method according to  claim 5 , wherein
 the course characteristic is an essentially linearly descending curve (linear descent) when the parameter type is the mean value and 
 the course characteristic is an essentially hill-shaped curve (hill shape) when the parameter type is the standard deviation or the CSI. 
 
     
     
       7. Method according to  claim 1 , comprising the following method steps:
 forming temporally sequential intervals that comprise temporally sequential parameter values (parameter intervals), parameter intervals being temporally sequential such that a following parameter interval differs from the preceding parameter interval in that it includes, in place of the oldest parameter value of the preceding parameter interval, the parameter value temporally subsequent to the most recent parameter value of the preceding parameter interval, 
 examining the parameter intervals one at a time to see whether the course characteristic occurs in the individual parameter interval. 
 
     
     
       8. Method according to  claim 1 , wherein a series comprises 4 to 300 or 4 to 5000 RR intervals. 
     
     
       9. Method according to  claim 1 , the series of temporally sequential parameter values differing in that the series that served as the basis for determining the following parameter value includes, in place of the oldest RR interval of the series that served as the basis for determining the preceding parameter value (preceding series), the RR interval temporally subsequent to the most recent RR interval of the preceding series. 
     
     
       10. Method for detecting epileptic or psychogenic seizures, comprising the steps of:
 a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, 
 b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values), 
 c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure, 
 further comprising the following method steps
 subdividing the parameter intervals into at least two sub-intervals 
 determining a supra-parameter value for each sub-interval 
 identifying on the basis of the supra-parameter values whether or not a course characteristic is present. 
 
 
     
     
       11. Method according to  claim 10 , wherein the at least two sub-intervals are of essentially equal size. 
     
     
       12. Method for detecting epileptic or psychogenic seizures, comprising the steps of:
 a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, 
 b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values), 
 c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure, 
 further comprising the following method steps:
 forming temporally sequential intervals that comprise temporally sequential parameter values (parameter intervals), parameter intervals being temporally sequential such that a following parameter interval differs from the preceding parameter interval in that it includes, in place of the oldest parameter value of the preceding parameter interval, the parameter value temporally subsequent to the most recent parameter value of the preceding parameter interval, 
 examining the parameter intervals one at a time to see whether the course characteristic occurs in the individual parameter interval, and 
 
 still further comprising the following method steps
 subdividing the parameter intervals into sub-intervals of essentially equal size 
 determining the mean of all the parameter values of each sub-interval 
 identifying the course characteristic “linearly descending” when the mean value of each older sub-interval is greater than the mean value of each more recent sub-interval. 
 
 
     
     
       13. Method according to  claim 12 , wherein the parameter intervals are subdivided into four sub-intervals of essentially equal size. 
     
     
       14. Method for detecting epileptic or psychogenic seizures, comprising the steps of:
 a. recording a large number of temporally sequential parameter values for a parameter type, wherein a parameter value is determined on the basis of a series of temporally sequential RR intervals, 
 b. comparing the time course of the parameter values with the time course of parameter values for the same parameter type that had been determined according to method step a, and the determination thereof is based on RR intervals that indicate a seizure (parameter reference values), 
 c. identifying a seizure when the time course of the parameter values exhibits a characteristic of the time course of the parameter reference values (course characteristic) indicating a seizure, 
 further comprising the following method steps:
 forming temporally sequential intervals that comprise temporally sequential parameter values (parameter intervals), parameter intervals being temporally sequential such that a following parameter interval differs from the preceding parameter interval in that it includes, in place of the oldest parameter value of the preceding parameter interval, the parameter value temporally subsequent to the most recent parameter value of the preceding parameter interval, 
 examining the parameter intervals one at a time to see whether the course characteristic occurs in the individual parameter interval, and 
 
 still further comprising the following method steps:
 subdividing the parameter intervals into four sub-intervals of essentially equal size, 
 determining the mean of all the parameter values of each sub-interval 
 subdividing the oldest sub-interval into four sub-intervals of equal size (sub-sub-intervals) and determining the mean of all the parameter values of each sub-sub-interval, 
 for the oldest sub-interval, examining whether the mean value for each more recent sub-sub-interval is greater than the mean value for each older sub-sub-interval (linear ascent in the oldest sub-sub-interval), 
 subdividing the most recent sub-interval into four sub-intervals of equal size (sub-sub-intervals) and determining the mean of all the parameter values of each sub-sub-interval, 
 for the most recent sub-interval, examining whether the mean value for each older sub-sub-interval is greater than the mean value for each more recent sub-sub-interval (linear descent in the most recent sub-sub-interval), 
 identifying the course characteristic “hill shape” when
 the oldest sub-sub-interval exhibits a linear ascent, 
 the most recent sub-sub-interval exhibits a linear descent, 
 the mean value of the oldest sub-interval is smaller than the mean value of the second-oldest sub-interval and 
 the mean value of the second-most recent sub-interval is greater than the mean value of the most recent sub-interval.